ENIGMA- Addiction: Pooling of Existing Datasets to Identify Brain and Genetic Correlates of Addiction

项目概要 ENIGMA 联盟 (http://enigma.ini.usc.edu/) 的成立是为了研究大脑的结构、功能、 通过结合来自多个站点的基因组和神经影像数据集来识别疾病。其目标是最大化 通过非常大的数据池工作获得现有数据集的统计能力和产量。这个目标有 鉴于对许多神经影像学和可重复性的严格性和可重复性的担忧，今天这一点变得更加重要 基因组发现。自从其最初的成功（一项关于皮层下体积的全基因组关联研究 超过 26,000 名参与者，Stein 等人，2012 Nature Genetics），许多工作组已 建立了使用标准化多站点 ENIGMA 预处理管道和分析方法来 研究特定疾病的神经生物学。该应用程序的 PI 创建了 ENIGMA Addiction 工作 该小组现在可以访问代表超过 14000 名参与者的数据集。基因组和神经影像学 对这一史无前例的数据收集的分析应该会对神经网络产生重要的新见解 和成瘾的遗传基础。在我们最初的概念验证资金 (R21DA038381) 的基础上，我们建议 进一步扩大成瘾工作组，该工作组目前仅包括世界上一小部分人 潜在的相关数据集，以确定用于遗传关联分析的强大的大脑依赖性标记， 并检查遗传和大脑标记，以了解整个物质使用阶段之间的过渡 寿命。我们还将扩大大脑测量的范围，包括结构和功能 连接性（DTI 和静息态数据）并将开发大脑结构的形态测量分析。我们 可以使用这些生物标志物来评估大脑改变是否先于依赖或在早期或 长期使用，如果通过利用家族、发育、 我们工作组的纵向和禁欲样本。我们将创建一个数据分析门户 提供对汇集数据的广泛访问和优化的分析方法，最大限度地提高严谨性和 再现性（例如，适当的协变量、嵌套方差模型、倾向权重 社会人口统计学、交叉验证），从而指导其他人适当、明智地使用这些数据。 分析门户将存档分析（例如，确切的主题和分析脚本）以确保最佳实践 和完全透明。我们将积极致力于扩大联合体，打造独一无二的大型联合体 神经影像-遗传成瘾数据集，我们将向研究界免费提供结果 通过在线交互工具 ENIGMA-Vis (http://enigma.ini.usc.edu/enigma-vis/)。

PROJECT SUMMARY The ENIGMA consortium (http://enigma.ini.usc.edu/) was established to investigate brain structure, function, and disease by combining genomic and neuroimaging datasets from multiple sites. Its goal is to maximize statistical power and the yield from existing datasets through very large data pooling efforts. This goal has added importance today in light of concerns over the rigor and reproducibility of many neuroimaging and genomic findings. Since its initial successes (a genome wide association study on subcortical volumes with over 26,000 participants, Stein et al., 2012 Nature Genetics), a number of working groups have been established which use the standardized multi-site ENIGMA preprocessing pipelines and analytic methods to study the neurobiology of specific diseases. This application's PIs created the ENIGMA Addiction working group which now has access to datasets representing over 14000 participants. Genomic and neuromaging analyses on this unprecedented collection of data should produce important new insights into the neural and genetic basis of addiction. Building on our initial proof of concept funding (R21DA038381), we propose to further expand the Addiction working group which currently includes only a fraction of the world's potential relevant datasets, to identify robust brain markers of dependence for genetic association analyses, and to examine genetic and brain markers for the transition between stages of substance use across the lifespan. We will also increase the range of brain measures examined to include structural and functional connectivity (DTI and resting-state data) and will develop morphometric analyses of brain structures. We can use these biomarkers to assess if brain alterations preceded dependence or arose during early or chronic use and if these effects correct with abstinence by exploiting the familial, developmental, longitudinal and abstinence samples in our working group. We will create a data analysis portal that will provide both wide access to the pooled data and optimized analytic methods that maximize rigor and reproducibility (e.g., appropriate covariates, nested variance models, propensity weighting for sociodemographics, cross-validation) thereby guiding others to use these data appropriately and wisely. The analysis portal will archive analyses (e.g., exact subjects and analysis scripts) to ensure best practice and full transparency. We will actively work to expand the consortium to create a uniquely large neuroimaging-genetic addiction dataset and we will make results freely available to the research community through the online interactive tool ENIGMA-Vis (http://enigma.ini.usc.edu/enigma-vis/).